Perceived COVID‐19 health and job risks faced by digital platform drivers and measures in place to protect them: A qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: As they deliver food, packages, and people across cities, digital platform drivers (gig workers) are in a key position to become infected with COVID-19 and transmit it to many others. The aim of this study is to identify perceived COVID-19 exposure and job risks faced by workers and document the measures in place to protect their health, and how workers responded to these measures. METHODS: In 2020-2021, in-depth interviews were conducted in Ontario, Canada, with 33 digital platform drivers and managers across nine platforms that delivered food, packages, or people. Interviews focused on perceived COVID-19 risks and mitigation strategies. Audio recordings were transcribed verbatim and uploaded to NVivo software for coding by varied dual pairs of researchers. A Stakeholder Advisory Committee played an instrumental role in the study. RESULTS: As self-employed workers were without the protection of employment and occupational health standards, platform workers absorbed most of the occupational risks related to COVID-19. Despite safety measures (e.g., contactless delivery) and financial support for COVID-19 illnesses introduced by platform companies, perceived COVID-19 risks remained high because of platform-related work pressures, including rating systems. We identify five key COVID-19 related risks faced by the digital platform drivers. CONCLUSION: We situate platform drivers within the broad context of precarious employment and recommend organizational- and government-level interventions to prevent digital platform worker COVID-19 risks and to assist workers ill with COVID-19. Measures to protect the health of platform workers would benefit public health aims by reducing transmission by drivers to families, customers, and consequently, the greater population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it